The COVID-19 pandemic is the defining global health crisis of our time and the greatest global humanitarian challenge the world has faced since World War II. The virus has spread widely, and the number of cases is rising daily as governments work to slow its spread. India has moved quickly, implementing a proactive, nationwide, lockdown, with the goal of flattening the curve and using the time to plan and resource responses adequately.
#Data wrangling
import pandas as pd
import numpy as np
import datetime
import requests
import warnings
#vizualization
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.dates as mdates
import seaborn as sns
import squarify
import plotly.offline as py
import plotly.graph_objects as go
import plotly_express as px
from IPython.display import Image
warnings.filterwarnings('ignore')
%matplotlib inline
#Loading Data
age_detail = pd.read_csv("D:/py/covid-in-india/AgeGroupDetails.csv")
india_covid_19 = pd.read_csv('D:/py/covid-in-india/covid_19_india.csv')
hospital_beds = pd.read_csv('D:/py/covid-in-india/HospitalBedsIndia.csv')
individual_details = pd.read_csv('D:/py/covid-in-india/IndividualDetails.csv')
state = pd.read_csv('D:/py/covid-in-india/states.csv')
state_wise = pd.read_csv("D:/py/covid-in-india/state_wise.csv")
state_wise_daily = pd.read_csv('D:/py/covid-in-india/state_wise_daily.csv')
district = pd.read_csv('D:/py/covid-in-india/districts.csv')
district_wise = pd.read_csv('D:/py/covid-in-india/district_wise.csv')
vaccine_statewise = pd.read_csv('D:/py/covid-in-india/covid_vaccine_statewise.csv')
case_time_series = pd.read_csv('D:/py/covid-in-india/case_time_series.csv')
icmr_labs = pd.read_csv('D:/py/covid-in-india/icmr_labs_statewise.csv')
icmr_test = pd.read_csv('D:/py/covid-in-india/icmr_rtpcr_tests_daily.csv')
population = pd.read_csv('D:/py/covid-in-india/Indian States Population and Area.csv')
vaccine_doses_statewise = pd.read_csv('D:/py/covid-in-india/vaccine_doses_statewise.csv')
tested_icpr = pd.read_csv('D:/py/covid-in-india/tested_numbers_icmr_data.csv')
statewise_test = pd.read_csv('D:/py/covid-in-india/StatewiseTestingDetails.csv')
total_recovered = state_wise.iloc[0,2]
total_death = state_wise.iloc[0,3]
total_active = state_wise.iloc[0,4]
labels = ['Active','Recovered','Deceased']
sizes = [total_active,total_recovered,total_death]
color= ['#66b3ff','green','red']
explode = []
for i in labels:
explode.append(0.3)
plt.figure(figsize= (15,10))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=9, explode =explode,colors = color)
centre_circle = plt.Circle((0,0),0.0,fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.title('COVID-19 Cases in India',fontsize = 20)
plt.axis('equal')
plt.tight_layout()
We can see that 97% cases have been recovered successfully til July 18, 2021.
delta_recoverd = state_wise.iloc[0,9]
delta_death = state_wise.iloc[0,10]
labels = ['Recovered','Deceased']
sizes = [delta_recoverd,delta_death]
color= ['green','red']
explode = []
for i in labels:
explode.append(0.05)
plt.figure(figsize= (12,8))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=10, explode =explode,colors = color)
centre_circle = plt.Circle((0,0),0.90,fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.title('Delta cases - Deaths Vs Recovery rate',fontsize = 20)
plt.axis('equal')
plt.tight_layout()
As we can see delta variant cases have a good recovery rate. So just maintain social distancing and use mask to avoid contact.
Stay safe & Stay Healthy :)
merege1 = pd.merge(left=state_wise, right=population, left_on='State', right_on='State')
sort_df= merege1.sort_values('Confirmed',ascending=False)
fig = px.bar(sort_df, x="State",
y="Confirmed",
text="Confirmed",
color = "Confirmed",
barmode="group",
labels={'Confirmed':'Total cases'},
title="State-wise Confirmed Cases in India")
fig.update_traces(texttemplate='%{text:.1s}', textposition='outside')
fig.update_layout(uniformtext_minsize=5,)
fig.show()
We can see Maharashtra is by far have highest number of Corona cases and Lakshadweep have least cases on record till 8 july 2021.
labels = list(age_detail['Age group'])
sizes = list(age_detail['TotalCases'])
explode = []
for i in labels:
explode.append(0.05)
plt.figure(figsize= (15,10))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=9, explode =explode)
centre_circle = plt.Circle((0,0),0.70,fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.title('India - Age Group wise Distribution',fontsize = 20)
plt.axis('equal')
plt.tight_layout()
We can see that the age group <40 is the most affected which is against the trend which says elderly people are more at risk of being affected. Only 17% of people >60 are affected.
labels = ['Missing', 'Male', 'Female']
sizes = []
sizes.append(individual_details['gender'].isnull().sum())
sizes.append(list(individual_details['gender'].value_counts())[0])
sizes.append(list(individual_details['gender'].value_counts())[1])
explode = (0, 0.1, 0)
colors = ['#ffcc99','#66b3ff','#ff9999']
plt.figure(figsize= (15,10))
plt.title('Percentage of Gender',fontsize = 20)
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%',shadow=True, startangle=90)
plt.axis('equal')
plt.tight_layout()
*80% of the patients gender information is missing. lets remove them and then analyze.
labels = ['Male', 'Female']
sizes = []
sizes.append(list(individual_details['gender'].value_counts())[0])
sizes.append(list(individual_details['gender'].value_counts())[1])
explode = (0.1, 0)
colors = ['#66b3ff','#ff9999']
plt.figure(figsize= (15,10))
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%',
shadow=True, startangle=90)
plt.title('Percentage of Gender (Ignoring the Missing Values)',fontsize = 20)
plt.axis('equal')
plt.tight_layout()
Men are the most affected accounting to 67%. But, remember we have ~80% data missing.
df = state_wise_daily.iloc[:,[1,2,3]]
df = df.rename(columns={"Date_YMD":"Date"})
df=df.pivot(index="Date",columns=["Status"], values="TT")
df.reset_index(level=0, inplace=True)
df['Date'] = pd.to_datetime(df['Date'])
plt.figure(figsize= (15,10))
plt.xticks(rotation = 0 ,fontsize = 11)
plt.yticks(fontsize = 10)
plt.xlabel("Dates",fontsize = 15)
plt.ylabel('Total cases',fontsize = 20)
plt.title("Total Confirmed, Active, Death in India" , fontsize = 20)
ax1 = plt.plot_date(y= df["Confirmed"],x= df["Date"],linestyle ='-',color = 'b', markersize=0)
ax2 = plt.plot_date(y= df["Recovered"],x= df["Date"],linestyle ='-',color = 'g', markersize=0)
ax3 = plt.plot_date(y= df["Deceased"],x= df["Date"],linestyle ='-',color = 'r', markersize=0)
plt.legend(["Confirmed", "Recovered", "Deceased"],loc="upper left")
plt.show()
top_5=df.sort_values('Date',ascending=False).head()
print("\n\tCases in India as of July 7, 2021\n\n")
print(top_5.to_string(index=False))
Cases in India as of July 7, 2021
Date Confirmed Deceased Recovered
2021-07-17 41283 517 42049
2021-07-16 38115 560 43878
2021-07-15 39071 544 39827
2021-07-14 41759 578 39293
2021-07-13 40314 625 42436
after_lockdown = df.iloc[464:]
after_lockdown.reset_index()
plt.figure(figsize= (15,10))
plt.xticks(rotation = 0 ,fontsize = 11)
plt.yticks(fontsize = 10)
plt.xlabel("Dates",fontsize = 15)
plt.ylabel('Total cases',fontsize = 20)
plt.title("After Lockdown 2021: Confirmed, Active, Death in India" , fontsize = 20)
ax1 = plt.plot_date(y= after_lockdown["Confirmed"],x= after_lockdown["Date"],linestyle ='-',color = 'b', markersize=0)
ax2 = plt.plot_date(y= after_lockdown["Recovered"],x= after_lockdown["Date"],linestyle ='-',color = 'g', markersize=0)
ax3 = plt.plot_date(y= after_lockdown["Deceased"],x= after_lockdown["Date"],linestyle ='-',color = 'r', markersize=0)
plt.legend(["Confirmed", "Recovered", "Deceased"],loc="best")
plt.show()
As we can see there is a fall in recovered rate. But there is not much difference in daily cases and death rate is also constant. Lets see death rate in detail.
plt.figure(figsize= (15,10))
plt.xticks(rotation = 0 ,fontsize = 11)
plt.yticks(fontsize = 10)
plt.xlabel("Dates",fontsize = 15)
plt.ylabel('Deaths',fontsize = 20)
plt.title("After Lockdown 2021: Death in India" , fontsize = 20)
ax3 = plt.plot_date(y= after_lockdown["Deceased"],x= after_lockdown["Date"],linestyle =':',color = 'r', markersize=5)
plt.show()
We could see there is a spike in death rate in 12 july (around 2000 deaths) but then a fall in last 5 days.
hospital_beds['total'].iloc[36]=hospital_beds['total'].iloc[36].replace(",", "")
hospital_beds['total'] = pd.to_numeric(hospital_beds['total'])
hospital_beds['beds in public hospitals'].iloc[36]=hospital_beds['beds in public hospitals'].iloc[36].replace(",", "")
hospital_beds['beds in public hospitals'] = pd.to_numeric(hospital_beds['beds in public hospitals'])
hospital_beds['No. of Rural hospitals'] = pd.to_numeric(hospital_beds['No. of Rural hospitals'])
hospital_beds['beds in Rural hospitals'] = pd.to_numeric(hospital_beds['beds in Rural hospitals'])
hospital_beds['No. of Urban hospitals'] = pd.to_numeric(hospital_beds['No. of Urban hospitals'])
hospital_beds['Beds in Urban hospitals'] = pd.to_numeric(hospital_beds['Beds in Urban hospitals'])
hospital_beds = hospital_beds.drop(columns=['PHC','CHC','SDH','DH'])
A = pd.melt(hospital_beds,
id_vars=['States'],
value_vars=['total', 'No. of Rural hospitals', 'No. of Urban hospitals'])
B = pd.melt(hospital_beds,
id_vars=['States'],
value_vars=['beds in public hospitals', 'beds in Rural hospitals', 'Beds in Urban hospitals'])
A = A.rename(columns={"variable": "Hospital_type", "value": "No_of_hospitals"})
B = B.rename(columns={"variable": "Hospital_beds", "value": "No_of_beds"})
df = pd.merge(left=A, right=B, left_on='States', right_on='States')
df.loc[df['Hospital_type']=='total', 'Hospital_type']='No. of Public hospitals'
plt.figure(figsize= (15,8))
plt.xticks(rotation = 90 ,fontsize = 11)
plt.yticks(fontsize = 10)
plt.title("No. of Care centres: Public, Rural, Urban" , fontsize = 20)
sns.barplot(x="States", y="No_of_hospitals", data=df.iloc[:-9], hue='Hospital_type')
plt.show()
As one could see the Highest Public, Rural and Urban Care Centres are in U.P., Panducherry and Mizoram. The least Public, Rural and Urban Care centres are in Lakshadweep, Tripura and Uttrakhand.
plt.figure(figsize= (15,8))
plt.xticks(rotation = 90 ,fontsize = 11)
plt.yticks(fontsize = 10)
plt.title("No. of Beds in Care centres: Public, Rural, Urban" , fontsize = 20)
sns.barplot(x="States", y="No_of_beds", data=df.iloc[:-9], hue='Hospital_beds')
plt.show()
As one could see the Highest Public, Rural and Urban beds in Care centres are in Tamilnadu, Mizoram and Rajasthan.
statewise_test['Date'] = pd.to_datetime(statewise_test['Date'])
plt.figure(figsize=(15,10))
plt.xticks(rotation = 90 ,fontsize = 11)
plt.yticks(fontsize = 10)
plt.title("No. of Samples Collected in States" , fontsize = 20)
sns.barplot(data=statewise_test,
x='State',
y='TotalSamples')
plt.show()
state_cases = india_covid_19.groupby('State/UnionTerritory')['Confirmed','Deaths','Cured'].max().reset_index()
#state_cases = state_cases.astype({'Deaths': 'int'})
state_cases['Active'] = state_cases['Confirmed'] - (state_cases['Deaths']+state_cases['Cured'])
state_cases["Death Rate (per 100)"] = np.round(100*state_cases["Deaths"]/state_cases["Confirmed"],2)
state_cases["Cure Rate (per 100)"] = np.round(100*state_cases["Cured"]/state_cases["Confirmed"],2)
state_cases.sort_values('Confirmed', ascending= False).fillna(0).style.background_gradient(cmap='Blues',subset=["Confirmed"])\
.background_gradient(cmap='Blues',subset=["Deaths"])\
.background_gradient(cmap='Blues',subset=["Cured"])\
.background_gradient(cmap='Blues',subset=["Active"])\
.background_gradient(cmap='Blues',subset=["Death Rate (per 100)"])\
.background_gradient(cmap='Blues',subset=["Cure Rate (per 100)"])
| State/UnionTerritory | Confirmed | Deaths | Cured | Active | Death Rate (per 100) | Cure Rate (per 100) | |
|---|---|---|---|---|---|---|---|
| 24 | Maharashtra | 6113335 | 123531 | 5872268 | 117536 | 2.020000 | 96.060000 |
| 20 | Kerala | 2996094 | 13960 | 2877557 | 104577 | 0.470000 | 96.040000 |
| 19 | Karnataka | 2859595 | 35526 | 2784030 | 40039 | 1.240000 | 97.360000 |
| 34 | Tamil Nadu | 2503481 | 33132 | 2435872 | 34477 | 1.320000 | 97.300000 |
| 1 | Andhra Pradesh | 1908065 | 12898 | 1861937 | 33230 | 0.680000 | 97.580000 |
| 39 | Uttar Pradesh | 1706818 | 22656 | 1682130 | 2032 | 1.330000 | 98.550000 |
| 41 | West Bengal | 1507241 | 17834 | 1472132 | 17275 | 1.180000 | 97.670000 |
| 12 | Delhi | 1434687 | 25001 | 1408853 | 833 | 1.740000 | 98.200000 |
| 8 | Chhattisgarh | 996359 | 13462 | 977893 | 5004 | 1.350000 | 98.150000 |
| 32 | Rajasthan | 952836 | 8942 | 942882 | 1012 | 0.940000 | 98.960000 |
| 29 | Odisha | 927186 | 4299 | 897362 | 25525 | 0.460000 | 96.780000 |
| 14 | Gujarat | 823964 | 10072 | 811699 | 2193 | 1.220000 | 98.510000 |
| 23 | Madhya Pradesh | 790042 | 9017 | 780578 | 447 | 1.140000 | 98.800000 |
| 15 | Haryana | 769030 | 9506 | 758442 | 1082 | 1.240000 | 98.620000 |
| 4 | Bihar | 722746 | 9612 | 711913 | 1221 | 1.330000 | 98.500000 |
| 5 | Bihar**** | 715730 | 9452 | 701234 | 5044 | 1.320000 | 97.970000 |
| 35 | Telangana | 628282 | 3703 | 613124 | 11455 | 0.590000 | 97.590000 |
| 31 | Punjab | 596736 | 16131 | 578590 | 2015 | 2.700000 | 96.960000 |
| 3 | Assam | 522267 | 4717 | 493306 | 24244 | 0.900000 | 94.450000 |
| 36 | Telengana | 443360 | 2312 | 362160 | 78888 | 0.520000 | 81.690000 |
| 18 | Jharkhand | 346038 | 5118 | 340365 | 555 | 1.480000 | 98.360000 |
| 40 | Uttarakhand | 340882 | 7338 | 332006 | 1538 | 2.150000 | 97.400000 |
| 17 | Jammu and Kashmir | 317481 | 4345 | 309554 | 3582 | 1.370000 | 97.500000 |
| 16 | Himachal Pradesh | 202945 | 3485 | 198134 | 1326 | 1.720000 | 97.630000 |
| 13 | Goa | 167823 | 3079 | 162787 | 1957 | 1.830000 | 97.000000 |
| 30 | Puducherry | 118227 | 1763 | 114673 | 1791 | 1.490000 | 96.990000 |
| 25 | Manipur | 73581 | 1218 | 66132 | 6231 | 1.660000 | 89.880000 |
| 37 | Tripura | 68612 | 701 | 63964 | 3947 | 1.020000 | 93.230000 |
| 7 | Chandigarh | 61752 | 809 | 60837 | 106 | 1.310000 | 98.520000 |
| 26 | Meghalaya | 52358 | 880 | 47173 | 4305 | 1.680000 | 90.100000 |
| 2 | Arunachal Pradesh | 37879 | 181 | 34525 | 3173 | 0.480000 | 91.150000 |
| 28 | Nagaland | 25619 | 503 | 23982 | 1134 | 1.960000 | 93.610000 |
| 27 | Mizoram | 22155 | 98 | 18383 | 3674 | 0.440000 | 82.970000 |
| 33 | Sikkim | 21403 | 309 | 19200 | 1894 | 1.440000 | 89.710000 |
| 21 | Ladakh | 20137 | 204 | 19733 | 200 | 1.010000 | 97.990000 |
| 10 | Dadra and Nagar Haveli and Daman and Diu | 10575 | 4 | 10532 | 39 | 0.040000 | 99.590000 |
| 9 | Dadra and Nagar Haveli | 10377 | 4 | 10261 | 112 | 0.040000 | 98.880000 |
| 22 | Lakshadweep | 9947 | 49 | 9643 | 255 | 0.490000 | 96.940000 |
| 6 | Cases being reassigned to states | 9265 | 0 | 0 | 9265 | 0.000000 | 0.000000 |
| 0 | Andaman and Nicobar Islands | 7487 | 128 | 7343 | 16 | 1.710000 | 98.080000 |
| 38 | Unassigned | 77 | 0 | 0 | 77 | 0.000000 | 0.000000 |
| 11 | Daman & Diu | 2 | 0 | 0 | 2 | 0.000000 | 0.000000 |
:: I'm going to take top 15 State Data for this.
all_state = list(india_covid_19['State/UnionTerritory'].unique())
all_state.remove('Unassigned')
latest = india_covid_19[india_covid_19['Date'] > '2021-05-15']
state_cases = latest.groupby('State/UnionTerritory')['Confirmed','Deaths','Cured'].max().reset_index()
latest['Active'] = latest['Confirmed'] - (latest['Deaths']- latest['Cured'])
state_cases = state_cases.sort_values('Confirmed', ascending= False).fillna(0)
states =list(state_cases['State/UnionTerritory'][0:15])
states_confirmed = {}
states_deaths = {}
states_recovered = {}
states_active = {}
states_dates = {}
for state in states:
df = latest[latest['State/UnionTerritory'] == state].reset_index()
k = []
l = []
m = []
n = []
for i in range(1,len(df)):
k.append(df['Confirmed'][i]-df['Confirmed'][i-1])
l.append(df['Deaths'][i]-df['Deaths'][i-1])
m.append(df['Cured'][i]-df['Cured'][i-1])
n.append(df['Active'][i]-df['Active'][i-1])
states_confirmed[state] = k
states_deaths[state] = l
states_recovered[state] = m
states_active[state] = n
date = list(df['Date'])
states_dates[state] = date[1:]
def calc_movingaverage(values ,N):
cumsum, moving_aves = [0], [0,0]
for i, x in enumerate(values, 1):
cumsum.append(cumsum[i-1] + x)
if i>=N:
moving_ave = (cumsum[i] - cumsum[i-N])/N
moving_aves.append(moving_ave)
return moving_aves
fig = plt.figure(figsize= (25,17))
plt.suptitle('5-Day Moving Average of Confirmed Cases in Top 15 States',fontsize = 30,y=1.0)
k=0
for i in range(1,15):
ax = fig.add_subplot(5,3,i)
plt.xticks(rotation = 90 )
ax.bar(states_dates[states[k]],states_confirmed[states[k]],label = 'Day wise Confirmed Cases ')
moving_aves = calc_movingaverage(states_confirmed[states[k]],5)
ax.plot(states_dates[states[k]][:-2],moving_aves,color='red',label = 'Moving Average',linewidth =3)
plt.title(states[k],fontsize = 20)
handles, labels = ax.get_legend_handles_labels()
fig.legend(handles, labels, loc='upper left')
k=k+1
plt.tight_layout(pad=3.0)
def calc_growthRate(values):
k = []
for i in range(1,len(values)):
summ = 0
for j in range(i):
summ = summ + values[j]
rate = (values[i]/summ)*100
k.append(int(rate))
return k
fig = plt.figure(figsize= (25,17))
plt.suptitle('Growth Rate in Top 15 States',fontsize = 30,y=1.0)
k=0
for i in range(1,15):
ax = fig.add_subplot(5,3,i)
plt.xticks(rotation = 90 )
growth_rate = calc_growthRate(states_confirmed[states[k]])
ax.plot_date(states_dates[states[k]][21:],growth_rate[20:],color = '#9370db',label = 'Growth Rate',linewidth =3,linestyle='-')
plt.title(states[k],fontsize = 20)
handles, labels = ax.get_legend_handles_labels()
fig.legend(handles, labels, loc='upper left')
k=k+1
plt.tight_layout(pad=3.0)
colors_list = ['cyan','teal']
states = individual_details['detected_state'].unique()
if len(states)%2==0:
n_rows = int(len(states)/2)
else:
n_rows = int((len(states)+1)/2)
plt.figure(figsize=(14,60))
for idx,state in enumerate(states):
plt.subplot(n_rows,2,idx+1)
y_order = individual_details[individual_details['detected_state']==state]['detected_district'].value_counts().index
try:
g = sns.countplot(data=individual_details[individual_details['detected_state']==state],y='detected_district',orient='v',color=colors_list[idx%2],order=y_order)
plt.xlabel('Number of Cases')
plt.ylabel('')
plt.title(state)
plt.ylim(14,-1)
except:
pass
plt.tight_layout()
plt.suptitle('District-Wise Detected Cases',fontsize = 10,y=1.0)
plt.show()
hospital_beds = pd.read_csv('D:/py/covid-in-india/HospitalBedsIndia.csv')
hospital_beds['PHC'].iloc[36]=hospital_beds['PHC'].iloc[36].replace(",", "")
hospital_beds['CHC'].iloc[36]=hospital_beds['CHC'].iloc[36].replace(",", "")
hospital_beds['beds in public hospitals'].iloc[36]=hospital_beds['beds in public hospitals'].iloc[36].replace(",", "")
hospital_beds['total'].iloc[36]=hospital_beds['total'].iloc[36].replace(",", "")
hospital_beds['DH'].iloc[36]=hospital_beds['DH'].iloc[36].replace(",", "")
hospital_beds['PHC'] = pd.to_numeric(hospital_beds['PHC'])
hospital_beds['CHC'] = pd.to_numeric(hospital_beds['CHC'])
hospital_beds['DH'] = pd.to_numeric(hospital_beds['DH'])
hospital_beds['beds in public hospitals'] = pd.to_numeric(hospital_beds['beds in public hospitals'])
hospital_beds['total'] = pd.to_numeric(hospital_beds['total'])
hospital_beds = hospital_beds.iloc[:-1]
top_10_primary = hospital_beds.nlargest(10,'PHC')
top_10_community = hospital_beds.nlargest(10,'CHC')
top_10_district_hospitals = hospital_beds.nlargest(10,'DH')
top_10_public_facility = hospital_beds.nlargest(10,'total')
top_10_public_beds = hospital_beds.nlargest(10,'beds in public hospitals')
plt.figure(figsize=(15,10))
plt.suptitle('Top 10 States in each Health Facility',fontsize=20)
plt.subplot(221)
plt.title('Primary Health Centers')
plt.barh(top_10_primary['States'],top_10_primary['PHC'],color ='#87479d');
plt.subplot(222)
plt.title('Community Health Centers')
plt.barh(top_10_community['States'],top_10_community['CHC'],color = '#9370db');
plt.subplot(224)
plt.title('Total Public Health Facilities')
plt.barh(top_10_community['States'],top_10_public_facility['total'],color='#9370db');
plt.subplot(223)
plt.title('District Hospitals')
plt.barh(top_10_community['States'],top_10_district_hospitals['DH'],color = '#87479d');
top_rural_hos = hospital_beds.nlargest(10,'No. of Rural hospitals')
top_rural_beds = hospital_beds.nlargest(10,'beds in Rural hospitals')
top_urban_hos = hospital_beds.nlargest(10,'No. of Urban hospitals')
top_urban_beds = hospital_beds.nlargest(10,'Beds in Urban hospitals')
plt.figure(figsize=(15,10))
plt.suptitle('Urban and Rural Health Facility',fontsize=20)
plt.subplot(221)
plt.title('Rural Hospitals')
plt.barh(top_rural_hos['States'],top_rural_hos['No. of Rural hospitals'],color = '#87479d');
plt.subplot(222)
plt.title('Urban Hospitals')
plt.barh(top_urban_hos['States'],top_urban_hos['No. of Urban hospitals'],color = '#9370db');
plt.subplot(223)
plt.title('Rural Beds')
plt.barh(top_rural_beds['States'],top_rural_beds['beds in Rural hospitals'],color = '#87479d');
plt.subplot(224)
plt.title('Urban Beds')
plt.barh(top_urban_beds['States'],top_urban_beds['Beds in Urban hospitals'],color = '#9370db');
state_test = pd.pivot_table(statewise_test, values=['TotalSamples','Negative','Positive'], fill_value=0, index='State', aggfunc='max')
state_names = list(state_test.index)
state_test['State'] = state_names
plt.figure(figsize=(15,10))
sns.set_color_codes("pastel")
sns.barplot(x="TotalSamples", y= state_names, data=state_test,label="Total Samples", color = '#9370db')
sns.barplot(x='Positive', y=state_names, data=state_test,label='Positive', color='r')
plt.title('Testing statewise insight',fontsize = 20)
plt.legend(ncol=2, loc="upper right", frameon=True);
The coronavirus is having a growing impact on the Indian economy. Here is an interesting insight on how an extenstion of the lockdown would affect the economy (Credits: McKincy & Company)
